This series of files compile analyses done for the specific analysis of Chapter 1, for the regional campaign of 2016.
All analyses have been done with PRIMER-e 6 and R 3.6.3.
Click on the table of contents in the left margin to assess a specific analysis.
Click on a figure to zoom it
We used data from subtidal ecosystems (see metadata files for more information). Only stations that have been sampled both for abiotic parameters and benthic species were included.
Selected variables for the analyses:
- Depth of the station: depth (only for ANCOVAs)
- Percentage of organic matter: om
- Percentage of gravel: gravel
- Percentage of sand: sand
- Percentage of silt: silt
- Percentage of clay: clay
- Concentration of arsenic: arsenic
- Concentration of cadmium: cadmium
- Concentration of chromium: chromium
- Concentration of copper: copper
- Concentration of iron: iron
- Concentration of manganese: manganese
- Concentration of mercury: mercury
- Concentration of lead: lead
- Concentration of zinc: zinc
- Specific richness: S
- Total density of individuals: N
- Shannon’s diversity: H
- Piélou’s evenness: J
Abundances of Mesodesma arctatum (Marc) and Cistenides granulata (Cgra) were also considered (see IndVal and SIMPER results).
As data is missing for metal concentrations outside BSI, two Designs have been used:
- Design 1: stations at BSI, CPC, BDA, MR with habitat parameters
- Design 2: stations at BSI with heavy metal concentrations.
1. Data manipulation
For the following analyses, independant variables are habitat parameters and heavy metal concentrations, dependant variables are diversity indices. Variables have been standardized by mean and standard-deviation.
1.1. Identification of outliers
To identify stations that are not consistent with the others, we used the multivariate Cook’s Distance (CD) on the uncorrelated variables. A significative threshold of 4 times the mean of CD has been established.
Design 1
We identified stations 60, 72, 80 and 96 as general outliers. They have been deleted for the following analyses of Design 1.

Design 2
We identified stations 108 and 110 as general outliers. They have been deleted for the following analyses of Design 2.

1.2. Correlations between parameters
Correlations have been calculated with Spearman’s rank coefficient.
Design 1
According to these results, the following variables are highly correlated (\(|\rho|\) > 0.80) so they have been considered together in the regressions of Design 1:
- silt and clay (clay deleted)
We decided to keep sand, even if it is correlated with om, to stay consistant with the 2014 campaign.
Correlation coefficients between habitat parameters (Design 1)
| om |
1 |
-0.068 |
-0.807 |
0.714 |
0.706 |
| gravel |
-0.068 |
1 |
-0.192 |
-0.37 |
-0.329 |
| sand |
-0.807 |
-0.192 |
1 |
-0.772 |
-0.768 |
| silt |
0.714 |
-0.37 |
-0.772 |
1 |
0.973 |
| clay |
0.706 |
-0.329 |
-0.768 |
0.973 |
1 |


Design 2
According to these results, the following variables are highly correlated (\(|\rho|\) > 0.80) so they have been considered together in the regressions of Design 2:
- cadmium and manganese (manganese deleted)
- copper, lead and zinc (copper and zinc deleted)
We decided to keep arsenic, even though it is correlated with the copper/lead/zinc group, to stay consistant with the 2014 campaign.
Correlation coefficients between heavy metals concentrations (Design 2)
| arsenic |
1 |
0.492 |
0.736 |
0.876 |
0.773 |
0.399 |
0.646 |
0.816 |
0.903 |
| cadmium |
0.492 |
1 |
0.757 |
0.41 |
0.766 |
0.881 |
0.154 |
0.708 |
0.663 |
| chromium |
0.736 |
0.757 |
1 |
0.712 |
0.825 |
0.767 |
0.463 |
0.85 |
0.879 |
| copper |
0.876 |
0.41 |
0.712 |
1 |
0.633 |
0.38 |
0.572 |
0.829 |
0.89 |
| iron |
0.773 |
0.766 |
0.825 |
0.633 |
1 |
0.755 |
0.429 |
0.745 |
0.842 |
| manganese |
0.399 |
0.881 |
0.767 |
0.38 |
0.755 |
1 |
0.105 |
0.584 |
0.628 |
| mercury |
0.646 |
0.154 |
0.463 |
0.572 |
0.429 |
0.105 |
1 |
0.627 |
0.545 |
| lead |
0.816 |
0.708 |
0.85 |
0.829 |
0.745 |
0.584 |
0.627 |
1 |
0.898 |
| zinc |
0.903 |
0.663 |
0.879 |
0.89 |
0.842 |
0.628 |
0.545 |
0.898 |
1 |


2. Permutational Analyses of Covariance
Results of univariate PermANCOVAs on parameters and multivariate PermANCOVA on the whole benthic community with depth as covariate are presented in the table below. Variables have been standardized by mean and standard-deviation, and taxon densities were (log+1) transformed.
| om |
|
S |
S |
{CPC BDA MR} |
| gravel |
|
|
|
All regions in the same group |
| sand |
|
|
S |
All regions in the same group |
| silt |
S |
|
S |
{BSI CPC BDA}, {BDA MR} |
| clay |
|
|
|
{BSI BDA MR}, {CPC MR} |
| S (1 mm) |
|
|
S |
{BSI CPC MR}, {CPC BDA MR} |
| N (1 mm) |
|
|
|
All regions in the same group |
| H (1 mm) |
|
s~ |
S |
{CPC BDA MR}, {BSI MR} |
| J (1 mm) |
|
|
|
{BSI CPC MR}, {CPC BDA MR} |
| ALL SPECIES (1 mm) |
|
S |
S |
|
3. Similarity and characteristic species
Let’s have a look at the \(\beta\) diversity within our conditions and sites.
Results of the PERMDISP routine are shown below (mean and SE of the deviation from centroid for each group, i.e. multivariate dispersion), along with the mean Bray-Curtis dissimilarity for each group. Taxon densities were (log+1) transformed and PRIMER was used to do the PERMDISP.
Mean within-group Bray-Curtis dissimilarity for each condition or site
| HI |
64.6 |
0.83 |
0.917 |
| R |
61.9 |
1.14 |
0.878 |
| BSI |
62.9 |
1.18 |
0.903 |
| CPC |
60.2 |
2.25 |
0.87 |
| BDA |
61.1 |
1.93 |
0.882 |
| MR |
58.2 |
2.12 |
0.835 |
No significative relationships were found for either factor by the PERMDISP (p = 0.069) or the pairwise tests.
The following analyses allowed to detect species as characteristic of each condition. We used results from PRIMER to justify further their choice.
## cluster indicator_value probability
## cistenides_granulata 1 0.2836 0.018
## macoma_calcarea 1 0.2326 0.002
## ennucula_tenuis 1 0.1860 0.018
## eudorellopsis_integra 1 0.1395 0.029
## mesodesma_arctatum 2 0.2342 0.007
## harmothoe_imbricata 2 0.1975 0.010
## glycera_alba 2 0.1212 0.039
## psammonyx_nobilis 2 0.1212 0.029
##
## Sum of probabilities = 50.871
##
## Sum of Indicator Values = 5.89
##
## Sum of Significant Indicator Values = 1.52
##
## Number of Significant Indicators = 8
##
## Significant Indicator Distribution
##
## 1 2
## 4 4
SIMPER results (mean Bray-Curtis between-group dissimilarity: 0.926)
| echinarachnius_parma |
0.0984 |
0.136 |
0.721 |
0.689 |
0.42 |
0.106 |
| mesodesma_arctatum |
0.07 |
0.129 |
0.542 |
0.605 |
0.0995 |
0.182 |
| cistenides_granulata |
0.0609 |
0.0948 |
0.643 |
0.176 |
0.565 |
0.248 |
| strongylocentrotus_sp |
0.0427 |
0.0758 |
0.563 |
0.27 |
0.249 |
0.294 |
| nephtys_caeca |
0.0425 |
0.0556 |
0.764 |
0.359 |
0.23 |
0.34 |
| limecola_balthica |
0.0313 |
0.0578 |
0.542 |
0.234 |
0.18 |
0.373 |
| scoloplos_armiger |
0.0295 |
0.065 |
0.453 |
0.14 |
0.256 |
0.405 |
| macoma_calcarea |
0.0274 |
0.0569 |
0.482 |
0 |
0.312 |
0.435 |
| harmothoe_imbricata |
0.0257 |
0.0583 |
0.44 |
0.217 |
0.0161 |
0.462 |
| amphipholis_squamata |
0.0238 |
0.0611 |
0.389 |
0.042 |
0.241 |
0.488 |
| protomedeia_grandimana |
0.0228 |
0.0538 |
0.424 |
0.183 |
0.169 |
0.513 |
| psammonyx_nobilis |
0.0189 |
0.0592 |
0.32 |
0.185 |
0 |
0.533 |
| thyasira_sp |
0.0186 |
0.0469 |
0.397 |
0.021 |
0.241 |
0.553 |
| ennucula_tenuis |
0.0185 |
0.0422 |
0.438 |
0 |
0.241 |
0.573 |
| mya_arenaria |
0.0174 |
0.034 |
0.513 |
0.063 |
0.168 |
0.592 |
| ciliatocardium_ciliatum |
0.014 |
0.045 |
0.312 |
0.0908 |
0.0766 |
0.607 |
| goniada_maculata |
0.0139 |
0.0354 |
0.391 |
0.021 |
0.173 |
0.622 |
| glycera_dibranchiata |
0.0134 |
0.043 |
0.31 |
0.021 |
0.0806 |
0.637 |
| glycera_alba |
0.0128 |
0.0408 |
0.313 |
0.172 |
0 |
0.65 |
| ameritella_agilis |
0.0117 |
0.0491 |
0.238 |
0 |
0.131 |
0.663 |
| astarte_undata |
0.0117 |
0.0388 |
0.301 |
0.142 |
0 |
0.676 |
| astarte_subaequilatera |
0.0106 |
0.0363 |
0.293 |
0.134 |
0 |
0.687 |
| nucula_proxima |
0.00992 |
0.0349 |
0.284 |
0 |
0.112 |
0.698 |
| pygospio_elegans |
0.00989 |
0.0449 |
0.22 |
0.137 |
0.0161 |
0.708 |
| ophelia_limacina |
0.00977 |
0.0299 |
0.327 |
0.042 |
0.0578 |
0.719 |
| diastylis_sculpta |
0.00966 |
0.0405 |
0.238 |
0.0488 |
0.0322 |
0.729 |
| eudorellopsis_integra |
0.00955 |
0.0267 |
0.358 |
0 |
0.153 |
0.74 |
| ampharetidae_spp |
0.00948 |
0.0277 |
0.342 |
0.0753 |
0.0535 |
0.75 |
| yoldia_myalis |
0.00913 |
0.0285 |
0.321 |
0.0543 |
0.0484 |
0.76 |
| nephtys_bucera |
0.00905 |
0.0256 |
0.354 |
0.063 |
0.0322 |
0.77 |
| ampeliscidae_spp |
0.00898 |
0.0253 |
0.354 |
0.063 |
0.0511 |
0.779 |
| pontoporeia_femorata |
0.00877 |
0.0404 |
0.217 |
0 |
0.132 |
0.789 |
| bipalponephtys_neotena |
0.00836 |
0.037 |
0.226 |
0 |
0.106 |
0.798 |
| maldanidae_spp |
0.00825 |
0.0272 |
0.303 |
0.0908 |
0.0322 |
0.807 |
| pagurus_pubescens |
0.00766 |
0.0231 |
0.331 |
0.0753 |
0.0161 |
0.815 |
| polynoidae_spp |
0.00756 |
0.0217 |
0.349 |
0.021 |
0.0952 |
0.823 |
| ampharete_oculata |
0.00725 |
0.0439 |
0.165 |
0.0666 |
0 |
0.831 |
| phyllodoce_mucosa |
0.00643 |
0.0241 |
0.267 |
0 |
0.106 |
0.838 |
| phyllodocidae_spp |
0.00629 |
0.0211 |
0.298 |
0.021 |
0.0484 |
0.845 |
| phoxocephalus_holbolli |
0.00621 |
0.0329 |
0.189 |
0 |
0.0827 |
0.851 |
| testudinalia_testudinalis |
0.00576 |
0.026 |
0.222 |
0.08 |
0 |
0.858 |
| harpinia_propinqua |
0.00547 |
0.0253 |
0.216 |
0.0753 |
0.0161 |
0.864 |
| quasimelita_formosa |
0.00486 |
0.0192 |
0.253 |
0 |
0.0739 |
0.869 |
| nephtys_ciliata |
0.00455 |
0.0213 |
0.214 |
0 |
0.0645 |
0.874 |
| platyhelminthes |
0.00429 |
0.0164 |
0.262 |
0 |
0.0484 |
0.878 |
| lacuna_vincta |
0.00427 |
0.0233 |
0.184 |
0 |
0.0417 |
0.883 |
| cancer_irroratus |
0.00405 |
0.0143 |
0.283 |
0.042 |
0.0161 |
0.887 |
| nephtys_incisa |
0.00399 |
0.0185 |
0.216 |
0.021 |
0.0161 |
0.892 |
| arrhoges_occidentalis |
0.00398 |
0.0167 |
0.239 |
0.0543 |
0 |
0.896 |
4. Univariate regressions
We used linear models for the all regressions on diversity indices. Outliers and correlated variables were removed from these analyses. Variables have been standardized by mean and standard-deviation (coefficients need to be back-transformed to be used in predictive models).
4.1. Simple regressions
These analyses have been do to explore the relationships between variables. As it is a huge number of results to interpret, only multiple regressions will be included in the article (see below).
Depth has been shown important for several parameters in the ANCOVAs, so here are the corresponding scatterplots.

Design 1
Adjusted R-squared of simple regressions for Design 1
| S |
0.09824 |
0.06215 |
0.0708 |
0.1258 |
| N |
0.01242 |
0.01491 |
0.03477 |
0.03467 |
| H |
0.09519 |
0.03329 |
0.06053 |
0.1134 |
| J |
0.004809 |
-0.0122 |
0.01178 |
0.01984 |
p-values of simple regressions for Design 1
| S |
0.00425 |
0.01962 |
0.01359 |
0.001309 |
| N |
0.1732 |
0.1542 |
0.06343 |
0.06371 |
| H |
0.004839 |
0.06765 |
0.02101 |
0.002229 |
| J |
0.2504 |
0.7054 |
0.1785 |
0.123 |
Design 2
Adjusted R-squared of simple regressions for Design 2
| S |
-0.01268 |
-0.04896 |
-0.03331 |
-0.04823 |
-0.047 |
0.06622 |
| N |
0.008407 |
-0.04909 |
-0.03615 |
-0.04682 |
-0.04877 |
0.03425 |
| H |
-0.01205 |
-0.03027 |
-0.001362 |
-0.02749 |
-0.02325 |
0.102 |
| J |
-0.04952 |
-0.01768 |
-0.0304 |
-0.03285 |
-0.03656 |
-0.04851 |
p-values of simple regressions for Design 2
| S |
0.4008 |
0.8897 |
0.5762 |
0.8559 |
0.8132 |
0.1303 |
| N |
0.2907 |
0.8964 |
0.6107 |
0.8078 |
0.8796 |
0.2014 |
| H |
0.3967 |
0.543 |
0.3361 |
0.5155 |
0.478 |
0.08065 |
| J |
0.9251 |
0.4348 |
0.5443 |
0.5708 |
0.6162 |
0.8677 |
4.2. Multiple regressions
This section presents analyses done to determine (i) which model (Design 1, Design 2) decribes the best the parameters and (ii) which variables are the most important to explain the parameters.
4.2.1. Best model selection
This step was not used here as each models are necessary.
4.2.2. Significative variables selection
We identified which variables were selected after an AIC procedure to predict the best the parameters. Results of the variable selection, according to AIC, are shown on the tables below:
- for the model of Design 1
| om |
|
|
|
|
| gravel |
|
- |
+ |
|
| sand |
+ |
- |
+ |
|
| silt/clay |
+ |
- |
+ |
+ |
| Adjusted \(R^{2}\) |
0.17 |
0.1 |
0.18 |
0.02 |
- for the model of Design 2
| arsenic |
|
|
|
|
| cadmium/manganese |
|
|
|
|
| chromium |
- |
- |
- |
|
| iron |
|
|
|
|
| mercury |
|
|
|
|
| lead/copper/zinc |
+ |
+ |
+ |
|
| Adjusted \(R^{2}\) |
0.29 |
0.16 |
0.21 |
0 |
Details of the regressions, with diagnostics and cross-validation, are summarized below.
Design 1
Richness
## FULL MODEL
## Adjusted R2 is: 0.15
Fitting linear model: S ~ om + gravel + sand + silt
| (Intercept) |
-0.07118 |
0.1104 |
-0.6444 |
0.5215 |
|
| om |
-0.03253 |
0.2084 |
-0.1561 |
0.8765 |
|
| gravel |
0.1478 |
0.3643 |
0.4057 |
0.6863 |
|
| sand |
1.23 |
0.9469 |
1.3 |
0.1982 |
|
| silt |
1.498 |
0.9953 |
1.505 |
0.137 |
|
## RMSE from cross-validation: 0.8980579
Variance Inflation Factors
| VIF |
2.01 |
2.35 |
8.23 |
9.4 |

## REDUCED MODEL
## Adjusted R2 is: 0.17
Fitting linear model: S ~ sand + silt
| (Intercept) |
-0.06123 |
0.1061 |
-0.5769 |
0.5659 |
|
| sand |
0.8883 |
0.4034 |
2.202 |
0.03102 |
* |
| silt |
1.143 |
0.371 |
3.081 |
0.002963 |
* * |
## RMSE from cross-validation: 0.8688591
Variance Inflation Factors
| VIF |
3.55 |
3.55 |

Density
## FULL MODEL
## Adjusted R2 is: 0.1
Fitting linear model: N ~ om + gravel + sand + silt
| (Intercept) |
0.08685 |
0.1204 |
0.7216 |
0.473 |
|
| om |
0.25 |
0.2271 |
1.101 |
0.2749 |
|
| gravel |
-1.125 |
0.397 |
-2.833 |
0.006085 |
* * |
| sand |
-2.733 |
1.032 |
-2.649 |
0.01006 |
* |
| silt |
-2.591 |
1.085 |
-2.389 |
0.01974 |
* |
## RMSE from cross-validation: 1.185244
Variance Inflation Factors
| VIF |
2.01 |
2.35 |
8.23 |
9.4 |

## REDUCED MODEL
## Adjusted R2 is: 0.1
Fitting linear model: N ~ gravel + sand + silt
| (Intercept) |
0.06937 |
0.1195 |
0.5805 |
0.5635 |
|
| gravel |
-0.9239 |
0.3531 |
-2.616 |
0.01094 |
* |
| sand |
-2.179 |
0.902 |
-2.416 |
0.0184 |
* |
| silt |
-1.858 |
0.8575 |
-2.166 |
0.03379 |
* |
## RMSE from cross-validation: 1.161344
Variance Inflation Factors
| VIF |
2.09 |
7.18 |
7.42 |

Diversity
## FULL MODEL
## Adjusted R2 is: 0.17
Fitting linear model: H ~ om + gravel + sand + silt
| (Intercept) |
-0.08487 |
0.111 |
-0.7647 |
0.4471 |
|
| om |
-0.1244 |
0.2094 |
-0.5938 |
0.5546 |
|
| gravel |
0.5965 |
0.3661 |
1.63 |
0.1079 |
|
| sand |
2.308 |
0.9514 |
2.426 |
0.01798 |
* |
| silt |
2.593 |
1 |
2.593 |
0.01168 |
* |
## RMSE from cross-validation: 0.8715363
Variance Inflation Factors
| VIF |
2.01 |
2.35 |
8.23 |
9.4 |

## REDUCED MODEL
## Adjusted R2 is: 0.18
Fitting linear model: H ~ gravel + sand + silt
| (Intercept) |
-0.07617 |
0.1095 |
-0.6957 |
0.489 |
|
| gravel |
0.4966 |
0.3235 |
1.535 |
0.1295 |
|
| sand |
2.032 |
0.8265 |
2.459 |
0.01649 |
* |
| silt |
2.228 |
0.7857 |
2.836 |
0.006011 |
* * |
## RMSE from cross-validation: 0.8738424
Variance Inflation Factors
| VIF |
2.09 |
7.18 |
7.42 |

Evenness
## FULL MODEL
## Adjusted R2 is: 0
Fitting linear model: J ~ om + gravel + sand + silt
| (Intercept) |
-0.006695 |
0.1222 |
-0.05477 |
0.9565 |
|
| om |
-0.1766 |
0.2307 |
-0.7655 |
0.4467 |
|
| gravel |
0.445 |
0.4032 |
1.104 |
0.2737 |
|
| sand |
1.277 |
1.048 |
1.219 |
0.2273 |
|
| silt |
1.533 |
1.102 |
1.391 |
0.1688 |
|
## RMSE from cross-validation: 1.022129
Variance Inflation Factors
| VIF |
2.01 |
2.35 |
8.23 |
9.4 |

## REDUCED MODEL
## Adjusted R2 is: 0.02
Fitting linear model: J ~ silt
| (Intercept) |
0.03868 |
0.1141 |
0.339 |
0.7356 |
|
| silt |
0.1809 |
0.1159 |
1.561 |
0.123 |
|
## RMSE from cross-validation: 0.9675419
Variance Inflation Factors
| VIF |
1 |

Design 2
Richness
## FULL MODEL
## Adjusted R2 is: 0.23
Fitting linear model: S ~ arsenic + cadmium + chromium + iron + mercury + lead
| (Intercept) |
0.2151 |
0.1867 |
1.152 |
0.2674 |
|
| arsenic |
-0.09907 |
0.3912 |
-0.2532 |
0.8035 |
|
| cadmium |
-0.06645 |
0.352 |
-0.1888 |
0.8528 |
|
| chromium |
-1.191 |
0.8019 |
-1.486 |
0.1581 |
|
| iron |
-0.456 |
0.5699 |
-0.8002 |
0.4361 |
|
| mercury |
-0.2986 |
0.2195 |
-1.361 |
0.1937 |
|
| lead |
2.031 |
0.6547 |
3.103 |
0.007277 |
* * |
## RMSE from cross-validation: 1.015118
Variance Inflation Factors
| VIF |
2.19 |
1.86 |
3.63 |
2.85 |
1.21 |
3.25 |

## REDUCED MODEL
## Adjusted R2 is: 0.29
Fitting linear model: S ~ chromium + lead
| (Intercept) |
0.1818 |
0.1772 |
1.026 |
0.3177 |
|
| chromium |
-1.538 |
0.5749 |
-2.675 |
0.01499 |
* |
| lead |
1.655 |
0.5249 |
3.153 |
0.005237 |
* * |
## RMSE from cross-validation: 0.8191663
Variance Inflation Factors
| VIF |
2.7 |
2.7 |

Density
## FULL MODEL
## Adjusted R2 is: 0.04
Fitting linear model: N ~ arsenic + cadmium + chromium + iron + mercury + lead
| (Intercept) |
0.1452 |
0.2174 |
0.6681 |
0.5142 |
|
| arsenic |
0.2008 |
0.4553 |
0.4409 |
0.6656 |
|
| cadmium |
0.05286 |
0.4098 |
0.129 |
0.8991 |
|
| chromium |
-1.021 |
0.9333 |
-1.094 |
0.2912 |
|
| iron |
-0.5391 |
0.6633 |
-0.8128 |
0.429 |
|
| mercury |
-0.2339 |
0.2554 |
-0.9155 |
0.3744 |
|
| lead |
1.531 |
0.762 |
2.009 |
0.06288 |
|
## RMSE from cross-validation: 1.361962
Variance Inflation Factors
| VIF |
2.19 |
1.86 |
3.63 |
2.85 |
1.21 |
3.25 |

## REDUCED MODEL
## Adjusted R2 is: 0.16
Fitting linear model: N ~ chromium + lead
| (Intercept) |
0.1392 |
0.1995 |
0.698 |
0.4936 |
|
| chromium |
-1.293 |
0.6472 |
-1.998 |
0.06024 |
|
| lead |
1.407 |
0.5909 |
2.381 |
0.0279 |
* |
## RMSE from cross-validation: 0.9483052
Variance Inflation Factors
| VIF |
2.7 |
2.7 |

Diversity
## FULL MODEL
## Adjusted R2 is: 0.06
Fitting linear model: H ~ arsenic + cadmium + chromium + iron + mercury + lead
| (Intercept) |
0.2134 |
0.205 |
1.041 |
0.3143 |
|
| arsenic |
-0.2515 |
0.4294 |
-0.5857 |
0.5668 |
|
| cadmium |
-0.04641 |
0.3864 |
-0.1201 |
0.906 |
|
| chromium |
-0.9912 |
0.8802 |
-1.126 |
0.2778 |
|
| iron |
-0.1605 |
0.6255 |
-0.2566 |
0.801 |
|
| mercury |
-0.1535 |
0.2409 |
-0.6373 |
0.5335 |
|
| lead |
1.71 |
0.7186 |
2.379 |
0.03107 |
* |
## RMSE from cross-validation: 0.8743848
Variance Inflation Factors
| VIF |
2.19 |
1.86 |
3.63 |
2.85 |
1.21 |
3.25 |

## REDUCED MODEL
## Adjusted R2 is: 0.21
Fitting linear model: H ~ chromium + lead
| (Intercept) |
0.1817 |
0.1848 |
0.9832 |
0.3379 |
|
| chromium |
-1.15 |
0.5997 |
-1.918 |
0.07024 |
|
| lead |
1.373 |
0.5476 |
2.508 |
0.02137 |
* |
## RMSE from cross-validation: 0.8518271
Variance Inflation Factors
| VIF |
2.7 |
2.7 |

Evenness
## FULL MODEL
## Adjusted R2 is: -0.23
Fitting linear model: J ~ arsenic + cadmium + chromium + iron + mercury + lead
| (Intercept) |
-0.06755 |
0.2492 |
-0.271 |
0.7901 |
|
| arsenic |
-0.1468 |
0.5221 |
-0.2811 |
0.7825 |
|
| cadmium |
0.1053 |
0.4698 |
0.2242 |
0.8256 |
|
| chromium |
0.7817 |
1.07 |
0.7304 |
0.4764 |
|
| iron |
0.1713 |
0.7605 |
0.2252 |
0.8248 |
|
| mercury |
0.2131 |
0.2929 |
0.7275 |
0.4781 |
|
| lead |
-0.8121 |
0.8737 |
-0.9295 |
0.3674 |
|
## RMSE from cross-validation: 1.327671
Variance Inflation Factors
| VIF |
2.19 |
1.86 |
3.63 |
2.85 |
1.21 |
3.25 |

## REDUCED MODEL
## Adjusted R2 is: 0
Fitting linear model: J ~ 1
| (Intercept) |
-0.04179 |
0.2191 |
-0.1908 |
0.8506 |
|
## RMSE from cross-validation: 1.05646
Quitting from lines 420-422 (C1_analyses_16B.Rmd) Error in Qr$qr[p1, p1, drop = FALSE] : indice hors limites De plus : There were 26 warnings (use warnings() to see them)
5. Multivariate regressions
Independant variables are habitat parameters or heavy metal concentrations, dependant variables are species abundances. Variables have been standardized by mean and standard-deviation, and outliers and correlated variables have been excluded. Taxon densities were (log+1) transformed.
This analysis has been done on PRIMER, with a DistLM to identify the variables that explain the most the community variability and with a dbRDA to plot the results.
Design 1

Variables selected by the DistLM procedure have a \(R^{2}\) of 0.08.
Design 2

Variables selected by the DistLM procedure have a \(R^{2}\) of 0.27.
⏪ | 📄
Taxon densities were (log+1) transformed.